synthetic  R Documentation 
Generates a synthetic version of a data.frame
, with
similar characteristics to the original. See Details for the algorithm used.
synthetic( data, model_expression = ranger(x = x, y = y), predict_expression = predict(model, data = xsynth)$predictions, missingness_expression = NULL, verbose = TRUE )
data 
A data.frame of which to make a synthetic version. 
model_expression 
An Rexpression to estimate a model. Defaults to

predict_expression 
An Rexpression to generate predicted values based
on the model estimated by 
missingness_expression 
Optional. An Rexpression to impute missing
values. Defaults to 
verbose 
Logical, Default: TRUE. Whether to show a progress bar while running the algorithm and provide informative messages. 
Based on the work by Nowok, Raab, and Dibben (2016), this function uses a simple algorithm to generate a synthetic dataset with similar characteristics to the original. The algorithm is as follows:
Let x be the original data.frame, with columns 1:j
Let xsynth be a synthetic data.frame, with columns 1:j
Column 1 of xsynth is a bootstrapped version of column 1 of x
Using model_expression
, a predictive model is built for column
c, for c along 2:j, with c predicted from columns 1:(c1) of the original
data.
Using predict_expression
, columns 1:(c1) of the synthetic data
are used to predict synthetic values for column c.
Variables are thus imputed in order of occurrence in the data.frame
.
To impute in a different order, reorder the data.
Note that, for data synthesis to work properly, it is essential that the
class
of variables is defined correctly. The default algorithm
ranger
supports numeric, integer, and factor types.
Other types of variables should be converted to one of these types, or users
can use a custom model_expression
and predict_expressio
when calling synthetic
.
Note that for data synthesis to work properly, it is essential that the
class
of variables is defined correctly. The default algorithm
ranger
supports numeric, integer, factor, and logical
data. Other types of variables should be converted to one of these types.
Users can provide use a custom model_expression
and
predict_expression
to use a different algorithm when calling
synthetic
.
As demonstrated in the example, users could call lm
as a
model_expression
to use
linear regression, which preserves linear marginal relationships but can give
rise to values out of range of the original data.
Or users could call sample
as a predict_expression
to bootstrap
each variable, a very quick solution that maintains univariate distributions
but loses all marginal relationships. These examples are not exhaustive, and
users can even create custom functions.
A data.frame
with synthetic data, based on data
.
Nowok, B., Raab, G.M and Dibben, C. (2016). synthpop: Bespoke creation of synthetic data in R. Journal of Statistical Software, 74(11), 126. doi: 10.18637/jss.v074.i11.
## Not run: # Example using the iris dataset and default ranger algorithm iris_syn < synthetic(iris) # Example using lm as prediction algorithm (only works for numeric variables) # note that, within the model_expression, a new data.frame is created because # lm() requires a separate data argument: dat < iris[, 1:4] synthetic(dat, model_expression = lm(.outcome ~ ., data = data.frame(.outcome = y, xsynth)), predict_expression = predict(model, newdata = xsynth)) ## End(Not run) # Example using bootstrapping: synthetic(iris, model_expression = NULL, predict_expression = sample(y, size = length(y), replace = TRUE)) ## Not run: # Example with missing data, no imputation iris_missings < iris for(i in 1:10){ iris_missings[sample.int(nrow(iris_missings), 1, replace = TRUE), sample.int(ncol(iris_missings), 1, replace = TRUE)] < NA } iris_miss_syn < synthetic(iris_missings) # Example with missing data, imputation by median/mode substitution # First, define a simple function for median/mode substitution: imp_fun < function(x){ if(is.data.frame(x)){ return(data.frame(sapply(x, imp_fun))) } else { out < x if(inherits(x, "numeric")){ out[is.na(out)] < median(x[!is.na(out)]) } else { out[is.na(out)] < names(sort(table(out), decreasing = TRUE))[1] } out } } # Then, call synthetic() with this function as missingness_expression: iris_miss_syn < synthetic(iris_missings, missingness_expression = imp_fun(data)) ## End(Not run)
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